Modeling of Single Photon Avalanche Diode Array Detectors for PET Applications
Bibliographic record
Abstract
We developed a configurable model of single photon avalanche diodes (SPAD) array photodetectors with intelligent control and active quenching. In this model individual components can be simulated independently and subsequently linked to provide the overall detector response. The model enables the simulation of the entire detector and analysis of performance, including photon detection efficiency, timing and energy resolution. It can be used to optimize detector performance for specific applications, such as positron emission tomography (PET). The simulator consists of multiple configurable and interchangeable modules to model the array geometry as well as physical and optical characteristics based on physical models and statistical equations. Readout electronics are also simulated in an algorithmic form. Monte Carlo simulations are used to model the 511 keV annihilation photon interactions and the optical photon transport in the scintillator, as well as carrier random walk in the silicon. Different methods to extract information from the digital output signal can be investigated. The simulator paves the way to the developement of new algorithms to extract relevant information in PET, but also for other applications such as Cerenkov radiation and fluorescence microscopy. Simulation results for photon detection efficiency, energy resolution and timing resolution are reported, showing the functionality of the simulator.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".